import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Union[str, Any]     					=		(DDPMScheduler,)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  **lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							__lowercase					     =				{
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**lowerCAmelCase__		)
							return config





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,  [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCAmelCase__ ,  beta_end=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							self.check_over_configs(thresholding=lowerCAmelCase__		)
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowerCAmelCase__ ,  prediction_type=lowerCAmelCase__ ,  sample_max_value=lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							for t in [0, 5_00, 9_99]:
											self.check_over_forward(time_step=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.0_0979		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				len(lowerCAmelCase__		)

							__lowercase					     =				self.dummy_model()
							__lowercase					     =				self.dummy_sample_deter
							__lowercase					     =				torch.manual_seed(0		)

							for t in reversed(range(lowerCAmelCase__		)		):
											# 1. predict noise residual
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		)

											# 2. predict previous mean of sample x_t-1
											__lowercase					     =				scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__lowercase					     =				pred_prev_sample

							__lowercase					     =				torch.sum(torch.abs(lowerCAmelCase__		)		)
							__lowercase					     =				torch.mean(torch.abs(lowerCAmelCase__		)		)

							assert abs(result_sum.item() - 258.9606		) < 1E-2
							assert abs(result_mean.item() - 0.3372		) < 1E-3





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config(prediction_type='''v_prediction'''		)
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				len(lowerCAmelCase__		)

							__lowercase					     =				self.dummy_model()
							__lowercase					     =				self.dummy_sample_deter
							__lowercase					     =				torch.manual_seed(0		)

							for t in reversed(range(lowerCAmelCase__		)		):
											# 1. predict noise residual
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		)

											# 2. predict previous mean of sample x_t-1
											__lowercase					     =				scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											__lowercase					     =				pred_prev_sample

							__lowercase					     =				torch.sum(torch.abs(lowerCAmelCase__		)		)
							__lowercase					     =				torch.mean(torch.abs(lowerCAmelCase__		)		)

							assert abs(result_sum.item() - 202.0296		) < 1E-2
							assert abs(result_mean.item() - 0.2631		) < 1E-3





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				[1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=lowerCAmelCase__		)

							__lowercase					     =				scheduler.timesteps

							for i, timestep in enumerate(lowerCAmelCase__		):
											if i == len(lowerCAmelCase__		) - 1:
															__lowercase					     =				-1
											else:
															__lowercase					     =				timesteps[i + 1]

											__lowercase					     =				scheduler.previous_timestep(lowerCAmelCase__		)
											__lowercase					     =				prev_t.item()

											self.assertEqual(lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				[1_00, 87, 50, 51, 0]

							with self.assertRaises(lowerCAmelCase__ ,  msg='''`custom_timesteps` must be in descending order.'''		):
											scheduler.set_timesteps(timesteps=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				[1_00, 87, 50, 1, 0]
							__lowercase					     =				len(lowerCAmelCase__		)

							with self.assertRaises(lowerCAmelCase__ ,  msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
											scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__ ,  timesteps=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self.scheduler_classes[0]
							__lowercase					     =				self.get_scheduler_config()
							__lowercase					     =				scheduler_class(**lowerCAmelCase__		)

							__lowercase					     =				[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    lowerCAmelCase__ ,  msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,  ):
											scheduler.set_timesteps(timesteps=lowerCAmelCase__		)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[Any]         =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[Any]     					=		'''audio-spectrogram-transformer'''





			def __init__(  self ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=1_28 ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				patch_size
							__lowercase					     =				qkv_bias
							__lowercase					     =				frequency_stride
							__lowercase					     =				time_stride
							__lowercase					     =				max_length
							__lowercase					     =				num_mel_bins